Evaluating the Impact of Data Quality on Machine Learning Model Performance

Abstract

This study examines in detail the significant impact of data quality on the performance of machine learning models. It explores multiple aspects of data quality by analyzing findings across datasets and machine learning algorithms—accuracy, completeness, consistency, and timeliness The findings are connected a direct link between high-quality data and improved model accuracy, robustness, and generalizability. In addition, the study suggests ways to reduce the negative effects of poor data quality on model results, and provides useful methods for data preprocessing. Highlighting the critical role of high-quality data, this review highlights the need to continuously maintain and improve data quality standards to improve machine learning modeling in real-world situations plant